Inventory Management in the Age of Big Data

We are on the verge of a major upheaval in the way inventory is managed. This revolution is a result of the availability of the huge amounts of real-time data that are now routinely generated on the internet and through the interconnected world of enterprise software systems and smart products. In order to make effective use of this new data and to stay competitive, managers will need to redesign their supply-chain processes.

I am talking about going beyond using traditional historical data on past sales and stockouts. It is now possible to link data generated by all product interactions (including orders, examinations, and reviews by actual and potential customers) and transactions generated by suppliers and competitors who connect via internet web sites and cloud portals. This data can be used by material-management systems to control ordering and distribution of products throughout a company’s extended supply chain. In addition, any data that is coincident with these product interactions, that is derived from the firm’s external environment, can also be accessed and linked.

How will this work? Advanced machine learning and optimization algorithms can look for and exploit observed patterns, correlations, and relationships among data elements and supply chain decisions – e.g., when to order a widget, how many widgets to order, where to put them, and so on. Such algorithms can be trained and tested using past data. They then can be implemented and evaluated for performance robustness based on actual realizations of customer demands. For example, does use of these data-driven tools lower cost and/or enhance customer service?

Why does this matter? The traditional paradigm for supply-chain management is to develop sophisticated tools to generate forecasts that accurately predict the value and the level of uncertainty of future demand. These forecasts are then used as an input to an optimization problem that evaluates trade-offs and respects constraints in order to come up with decisions about managing materials. This two-step process, which is embodied in all current material-management planning and control systems, can be replaced by a single-step process that looks for the best relationship among all of the data and the decisions. Based on learning from the past, a “best” relationship can be identified, which will generate decisions, as future uncertainty is resolved, that are better than the decisions derived from the traditional two-step approach of first forecast and then optimize.

This approach is not restricted by any a priori assumptions about the nature of the market and the behaviors that lead to customer demands or about the trade-0ffs and constraints that have to be considered in order to evaluate material-management decisions. Instead, the power of computer learning, supplemented by management input based on context-specific knowledge, is used to find the best relationship between all possible decisions and full range of the data. Use of this relationship can lead to better operational performance. It will lead to better outcomes because it utilizes all of the data available to current methods along with extensive additional data that currently is ignored and which may be relevant.

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This scenario becomes even more compelling when the impact of the internet of things is factored in. As they are used by customers, smart, connected products can generate orders of magnitude more data about current operating conditions and real-time performance of products. This data, along with traditional historical sales-based data, can support better methods for maintenance and the replacement of products.

The approach described here extends the concept of prescriptive analytics, which is considered by many to be the ultimate use of Big Data. Prescriptive analytics, however, has eluded most users of Big Data to date. There are some notable exceptions in industries such as online apparel retailing, where companies can view real-time, customer-purchase decisions (e.g., to buy or not to buy) and also can change the price of each product frequently at a negligible cost. The online retailer, however, knows little about the probability that consumers will purchase at each prices it sets but can learn dynamically about expected demand from sales data.

While many challenges remain, it is clear that a new approach that exploits all of the data that is becoming available is inevitable, given the connectivity, capacity, and transparency of data sources along with the vast computing power and data storage capacity available at a low cost. Like all planning systems, the proof will be in the results, when intelligent systems based on this approach are applied in practice. Change is coming to the world of inventory management and those that embrace this change will be ahead of the game. Successful adoption of this change will require active involvement of multiple functions within the firm along with a high level of coordination with both upstream and downstream supply chain partners as well as engagement with customers.

Morris A. Cohen is the Panasonic Professor of Manufacturing and Logistics at the University of Pennsylvania’s Wharton School and cofounder of MCA Solutions, a software company specializing in after-sales logistics planning systems that recently merged with PTC, a software company that helps manufacturers create, operate, and service products.